1. Field of the Invention
The present invention is directed to optimizing operation of a system having many interdependent variable operating conditions which may have non-linear relationships and, more particularly, to optimizing the operation of an energy producing system having a plurality of interrelated components.
2. Description of the Related Art
Optimization techniques are commonly used to maximize production or minimize cost of operating industrial processes, particularly with regard to the consumption of energy and the correlated production output. Several techniques have been applied to chemical engineering to establish operating conditions that yield a maximum return on investment while minimizing operating costs. These techniques include linear programming and evolutionary operation. Application of these techniques to optimizing cogeneration boiler systems for producing steam and electricity are disclosed in U.S. Pat. Nos. 4,604,714 and 4,805,114 to R. E. Putman et al. which are incorporated herein by reference.
Of the two techniques mentioned above, linear programming is fairly well known, while evolutionary operation is much less commonly used since it was originally developed as a manual system in the 1960's when powerful online computers were not available. Linear programming application software is available commercially, e.g., LP88 from Eastern Software Products, Inc. of Alexandria, Virginia. A particularly useful form of evolutionary operation was described in "Process Improvement with SIMPLEX Self-Directing Evolutionary Operation" by E. H. Carpenter and H. C. Sweeney, Chemical Engineering. Jul. 5, 1965, pp. 117-126. The evolutionary operation technique described in this article will be referred to herein as SSDEVOP.
The SSDEVOP technique modifies the setting of several key process variables to find a setting for an industrial process which results in maximum production or minimum costs, while taking into account predetermined constraints of the variables. The selected process variables are assigned different values in a set of experiments which in the original technique are conducted on the actual industrial process. The results are compared and a new base case is produced by averaging the setting of each process variable for all except the test which produced the worst case results, doubling this average and subtracting the worst case results. This base case is then used to produce a new set of tests by adding or subtracting small amounts to or from the setting of each variable. The results of the tests in a set of experiments is analyzed to find new best and worst cases and to produce a new base case and a new set of experiments from that base case. The process is repeated until there is little improvement in the best result. At this time, the amount of change from one test to another in a set of tests can be reduced for a new set of tests so that a more precise optimum setting for the process can be determined. The '714 and '114 patents teach application of SSDEVOP to the process of generating steam and electricity in a cogeneration system. At the end of the '114 patent it is suggested that SSDEVOP be used to optimize load and fuel distribution in pairs of boilers which are part of a much larger system where the load and fuel distribution of the large system is determined by a linear programming matrix. However, there is no suggestion of how to optimize the performance of a process having many non-linear relationships between process variables. A linear programming matrix can handle many variables, but by definition does not handle non-linear relationships. The SSDEVOP technique handles non-linear relationships, but becomes unwieldy as the number of selected variables increases beyond four.